Multivariate statistical brain electromagnetic mapping.

Abstract

Brain Electromagnetic Topography (BET) has attained widespread use. The representation of EEG or MEG parameters as scalp maps (BETm) aids its clinical interpretation. However, some critical issues limit the usefulness of BETm. In particular, the conventional statistical assessment of BETm with respect to normative data is based upon marginal significance probability scales which involve multiple univariate comparisons (one at each recording site). As a consequence, the probability of false positive findings (type I error) is increased above its nominal level. The use of conservative levels avoids this phenomenon but results in a considerable increase of the probability of not detecting real abnormality (type II error). Furthermore, BETm are constructed without taking into consideration the patterns of correlations characteristic of electromagnetic data under normal states of brain functioning. This limits the capability of BETm of representing multivariate aspects of abnormality. This paper introduces some techniques to approach these difficulties. Multivariate Brain Electromagnetic Topographic maps (MBETm) are defined, which retain the attractive features of mapping but also take advantage of multivariate characteristics (in the spatial and frequency domains) to highlight aspects of neuropathology. Moreover, simultaneous significance probability (SSP) scales, valid for both BETm and MBETm, are introduced for the global control of the probability of a type I error. The use of these techniques is illustrated with data from patients with cortical tumours and with epilepsy. ROC analysis shows that in some cases there is a significant improvement in both detection and localization accuracy.